Communication systems deal with the transmission of information from a transmitter to a receiver. The transmission medium through which the information passes often contains many sources of noise, including cosmic radiation, Additive White Gaussian Noise (AWGN), Rayleigh scattering (multipath propagation) and electromagnetic noise. The presence of these noise sources corrupts or prevents the transmission of the desired information, thus limiting the ability to communicate.
It is well known in the art that coding of the information to be transmitted, through the addition of redundant information calculated from the source information, improves the ability to successfully receive the transmitted information. Decoding uses the redundant information to detect the presence of errors or estimate the most probable emitted bits, given those received. Errors are detected when the transmitted redundancy is different from that subsequently calculated with the received data.
The weight of a codeword is a measure of the capacity to recover data from the codeword. A codeword with a high number of bits has a high weight. A low weight codeword exhibits a low ability to recover data, whereas, conversely, a high weight codeword exhibits improved recovery of data.
Automatic-repeat-request (ARQ) coding schemes employ an error-detection code. If the presence of an error is detected in the information received, a message requesting retransmission of the relevant information is sent from the receiver to the transmitter. ARQ coding schemes are relatively simple, but require the use of a feedback channel and deliver variable and comparatively slow throughput.
Forward error correction (FEC) coding schemes are used to encode information in systems in which propagation delays and latency are of concern. The receiver is able to detect and correct errors, without requiring a feedback channel.
Coding schemes can be broadly categorised into block codes and convolutional codes.
Block codes map a message of k information bits into a structured sequence of n bits, where n>k. The code is referred to as a (n,k) code. The ratio (n−k)/k is called the redundancy of the code and the ratio of information bits to the total number of bits, k/n, is called the code rate. The extra bits inserted provide redundancy and are used by the decoder to provide error detection and correction. The redundant bits added during encoding are only dependent on the k information bits in the message block. Block codes are often used to detect errors when ARQ is implemented.
Convolutional encoding generates a block of n code bits in a given period of time from k information bits, where n and k are typically small. The block of n bits generated by the encoder is dependent not only on the k information bits of the time period, but also on the message blocks generated during a predefined number of preceding time periods. The memory thus imparted on the coding enables errors to be corrected based on allowable sequences of codes. Convolutional decoding may be performed using either a Viterbi algorithm or LogMAP algorithm.
Convolutional codes are preferred for wireless voice communications systems in which the retransmission of data and its associated delay is intolerable. Block codes are capable of delivering higher throughput and are preferred for the transmission of data where latency is less of a concern.
Turbo codes, also known as parallel concatenated codes, are a class of codes whose performance is very close to the Shannon capacity limit. Turbo coders are implemented by connecting convolutional encoders either in parallel or series to produce concatenated outputs. Bit sequences passing from one encoder to another are permuted by an interleaver. In this manner, low-weight code words produced by a single encoder are transformed into high-weight code words. Turbo decoding thus takes two low weight codewords and obtains the effect of a much higher weight codeword.
At present, consumer wireless communication systems are primarily concerned with the transmission of voice. Such wireless communication systems include Advanced Mobile Phone Service (AMPS), Global System for Mobile Communication (GSM) and Code Division Multiple Access (CDMA). These represent the first (1G) and second (2G) generation systems. With the convergence of data and voice communication systems, the second-and-a-half generation (2.5G) and third generation (3G) systems are emerging in which the transmission of data is becoming a more important concern. In order to achieve superior error performance at higher transmission rates, turbo block encoding is preferred. The latency endemic to block coding is not as significant an issue as it is with the transmission of voice. New, third generation mobile wireless standards, like Universal Mobile Telecommunication Service (UMTS) and CDMA2000 require turbo encoding for data streams and convolutional encoding for voice streams. These systems require a complex turbo decoder for data and a Viterbi decoder for voice. Furthermore, backward compatibility requires that second generation standards are also supported.
The transmission of voice and data provides conflicting requirements of transmission rate versus latency and propagation delay. The current mode of addressing these problems is to provide separate encoding systems: turbo encoding for data streams and convolutional encoding for voice streams. Consequently, different decoders are also required, resulting in a multiplicity of hardware platforms and thus increased costs for telecommunications operators.
Butterfly processors typically consist of a number of Add-Compare-Select units and a Branch Metric Calculator. Known butterfly processors are capable of performing one of Viterbi or LogMap calculations. Consequently, in order to perform Viterbi and LogMap calculations, two distinct types of butterfly processors are required, resulting in additional hardware costs.